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Further evidence on sparse grids-based numerical integration in the mixed logit model

Author

Listed:
  • Zsolt Sándor

    (Sapientia Hungarian University of Transylvania)

Abstract

We study the performance of Gauss quadrature methods based on sparse grids for approximating integrals involved in mixed logit models. In Monte Carlo experiments we consider data generating processes in which consumer heterogeneity has low variance and data generating processes in which it has high variance. In the former case we find that, in line with previous literature, sparse grids produce very accurate estimates even when the number of points used for approximating integrals is small. However, in the latter case sparse grids yield biased estimates and are outperformed by quasi-Monte Carlo methods.

Suggested Citation

  • Zsolt Sándor, 2019. "Further evidence on sparse grids-based numerical integration in the mixed logit model," Economics Bulletin, AccessEcon, vol. 39(4), pages 2726-2731.
    • Handle: RePEc:ebl:ecbull:eb-19-00670
    as

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    References listed on IDEAS

    as
    1. Sándor, Zsolt & Train, Kenneth, 2004. "Quasi-random simulation of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 313-327, May.
    2. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    3. Heiss, Florian & Winschel, Viktor, 2008. "Likelihood approximation by numerical integration on sparse grids," Journal of Econometrics, Elsevier, vol. 144(1), pages 62-80, May.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    5. Chiou, Lesley & Walker, Joan L., 2007. "Masking identification of discrete choice models under simulation methods," Journal of Econometrics, Elsevier, vol. 141(2), pages 683-703, December.
    6. Brunner, Daniel & Heiss, Florian & Romahn, André & Weiser, Constantin, 2017. "Reliable estimation of random coefficient logit demand models," DICE Discussion Papers 267, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    discrete choice; random coefficients; simulation; quasi-Monte Carlo; panel;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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